Methods of using brain temporal dynamics

ABSTRACT

A method for treating depression in a subject in need thereof is provided. The method includes treating the subject by seizure therapy administered through electroconvulsive therapy (ECT) or magnetic seizure therapy (MST). The method further includes evaluating change in complexity of temporal dynamics in the brain of the subject, following treatment, to identify whether complexity of fine time scale temporal dynamics in the fronto-central and/or parieto-occipital region is reduced following treatment. The reduced complexity of fine time scale temporal dynamics in the fronto-central and/or parieto-occipital region following treatment identifies the subject as a responder to the seizure therapy for treating depression. The step of evaluating further comprises identifying change in complexity of coarse scale temporal dynamics in a parieto-central region following treatment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of Canadian patentapplication No. 2,950,616, filed Dec. 5, 2016.

FIELD OF INVENTION

The present invention relates generally to brain temporal dynamics. Morespecifically, the present invention relates to modulating the complexityof temporal dynamics in the brain for the treatment of depression.

BACKGROUND

Major depression is a leading cause of disability affecting over 350million people globally (Murray and Lopez, 1996). Over a third of thesepatients fail responding to medications. Dating back to 1700s, theinduction of seizures has been used to treat severe psychiatricconditions such as depression. Introduced in 1930s, seizure therapyadministered through electroconvulsive therapy (ECT) still remains themost effective treatment for depression (The UK ECT Review Group, 2003)even when many other antidepressant treatments have failed. However, thecognitive side effects of ECT (Lisanby et al., 2000, McClintock et al.,2014) limit its widespread use. Magnetic seizure therapy (MST) is anemerging antidepressant treatment that involves the induction of seizurethrough the administration of transcranial magnetic stimulation (TMS)(Moscrip et al., 2006; Hoy and Fitzergerald, 2010; McClintock et al.,2013). This approach to seizure induction causes less memory impairmentthan ECT (McClintock et al., 2013) and early treatment studies reportefficacy in depression (Kayser et al., 2015). Despite decades ofresearch, the biological targets of seizure therapy for depressionremain unclear. This has hindered the progress in development of newantidepressant interventions that have comparable efficacy to ECTwithout the cognitive side effects. Here, we propose a novel approach inexamining the biological target of seizure therapy by assessing theimpact of seizure on the temporal fluctuations (i.e., dynamics) of brainsignals.

Seizure is a biological phenomenon that significantly impacts braindynamicity visualized through electroencephalography (EEG). It isincreasingly evident that temporal fluctuations and variability observedin biological systems such as brain signals have a fundamental role inshaping the brain's capacity for information processing (Tononi et al.,1994; Tononi and Edelman, 1998; Sporns et al., 2000; Costa et al.,2005). This temporal fluctuation, occasionally referred to as biological“noise”, is distinct from random noise and structurally rich (Costa etal., 2005) exhibiting varying degree of recurring patterns (Costa etal., 2005). The less recurring temporal patterns, the more complex andunpredictable the signal is. In the brain, the complexity of signals atfine (smaller time increment) and coarse (larger time increments)time-scales is proposed to arise from transient increases and decreasesin correlated activity among local and distributed brain regions,subserving, integration and segregation of information at differentspatiotemporal scales (Sporns et al., 2000; McIntosh et al., 2014).While majority of existing experiments have quantified the strength offunctional coupling between brain regions and its disturbance indisorders of mood and consciousness (Fox et al., 2012; Kaiser et al.,2015; Sale et al.; 2015), emerging evidence points to the abnormalitiesin the temporal complexity of brain signals in disorders of affect andcognition (McIntosh et al., 2014).

SUMMARY OF INVENTION

In an embodiment, there is provided herein a use of a seizure ornon-seizure modality for modulating the complexity of temporal dynamicsin the brain for the treatment of depression as described substantiallyherein.

In another embodiment, there is provided herein a use of the complexityof temporal dynamics in the brain to monitor the efficacy ofanti-depression treatments as described substantially herein.

In another embodiment, there is provided herein a use of the complexityof temporal dynamics in the brain to monitor the specificity ofanti-depression treatments as described substantially herein.

In another embodiment, there is provided herein a use of the complexityof temporal dynamics of the brain to enhance cognition in a subjectduring anti-depression treatments as described substantially herein.

In another embodiment, there is provided herein a use of a seizure ornon-seizure modality for modulating the complexity of temporal dynamicsin the brain for the treatment of depression, wherein:

-   -   a) a subject with symptoms of depression is treated with a        seizure therapy modality, for example ECT or MST, or a        non-seizure modality such as rTMS, in order to decrease the        complexity of temporal dynamics of the brain as determined        against the individual's baseline, for example through EEG,    -   b) the modality is specifically targeted to at least one area of        the brain, such as the occipital and parieto-central regions of        the brain, and    -   c) the complexity of the temporal dynamics of the brain is        evaluated; and    -   d) following the treatment the symptoms of depression are        decreased while the deleterious cognitive effects of seizure        therapy are limited or reduced,    -   wherein, specifically, the significant reduction of the        complexity of fine time scale temporal dynamics in the occipital        region is indicative of a successful therapeutic outcome and the        significant reduction of the complexity of coarse scale temporal        dynamics in the parieto-central region is indicative of the        successful limitation of the deleterious cognitive side-effects        of the seizure therapy.

In another embodiment there is provided herein a use of the measurementof complexity of temporal dynamics in the brain to monitor the efficacyof anti-depression treatments, wherein

-   -   a) a baseline EEG is made of a subject exhibiting the symptoms        of depression before and following a given treatment, such as a        seizure treatment like ECT or MST, or non-seizure modality such        as rTMS or pharmacological means    -   b) and the complexity of fine scale temporal dynamics in the        occipital areas of the brain is evaluated wherein a significant        reduction of the fine scale (less than 30 factors, preferably        less than 20 factors) temporal dynamics in the occipital areas        of the brain this is indicative of a successful depression        treatment outcome, wherein the occipital regions encompass at        least the right occipital pole.

In another embodiment, there is provided herein a use of the measurementof complexity of temporal dynamics in the brain to monitor thespecificity of anti-depression treatments, wherein the post-treatmentcoarse time scale complexity is indicative of a treatment-inducedcognitive decline, wherein:

-   -   a) a baseline EEG measurement is made of a subject exhibiting        the symptoms of depression before and following a given        treatment, such as a seizure treatment like ECT or MST or a        non-seizure treatment such as rTMS or a pharmaceutical        treatment, and    -   b) the complexity of coarse scale temporal dynamics of greater        than 50 factors (scales determined by setting the appropriate        EEG sampling rate data processing), preferably less than 70        factors, in the parieto-central areas of the brain is evaluated,    -   wherein the lack of a significant change of the coarse scale        temporal dynamics in the parieto-central areas of the brain        post-treatment is indicative of the specificity of the        intervention, in that the anti-depression treatment does not        affect cognition and is indicative of a specific treatment, and        conversely a significant increase in the coarse scale temporal        dynamics in the parieto-central areas of the brain is indicative        of a cognitive deterioration, and decreased specificity of        treatment.

In another embodiment, there is provided herein a use of the measurementof complexity of temporal dynamics in the brain to enhance cognition ina subject during anti-depression treatments, wherein the coarse timescale complexity is indicative of a negative treatment induced impact oncognition, wherein:

-   -   a) a baseline EEG measurement is made of a subject exhibiting        the symptoms of depression before and following a given        treatment, such as a seizure treatment like ECT or MST or a        non-seizure treatment such as rTMS or a pharmaceutical        treatment, and    -   b) the complexity of coarse scale temporal dynamics of greater        than 50 factors (scales are to be determined by setting the        appropriate EEG sampling rate data processing), preferably less        than 70 factors, in the parieto-central areas of the brain is        evaluated,    -   wherein a significant reduction of the coarse scale temporal        dynamics in the parieto-central areas of the brain is indicative        of the positive impact of the intervention on cognition and is        indicative of a successful cognition-enhancing treatment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the Effect of Seizure Therapy on Complexity of TemporalDynamics.

FIG. 2 depicts the Effect of Seizure Therapy on Complexity in the SourceSpace.

FIG. 3 shows the Association between Modulation of Temporal Complexityand Mood and Cognition.

FIG. 4 shows a Region-Specific Change in Temporal Complexity PredictsChange in Mood and Cognition.

FIG. 5 shows a Seizure induced Modulation of Complexity and ItsAssociation with Mood and Cognition in the Source Space.

FIG. 6 shows a Prediction of Change in Mood and Cognition in the SourceSpace.

FIG. 7 (also referred to as Figure S1) shows the Effect of SeizureTherapy on Cortical Oscillations.

FIG. 8 (also referred to as Figure S2) shows The Association betweenCortical Oscillations and Mood.

FIG. 9 (also referred to as Figure S3) depicts The Association betweenCortical Oscillations and Cognition.

FIG. 10 (also referred to as Figure S4) shows the Effect of SeizureTherapy on Cortical Oscillations in Source Space.

FIG. 11 (also referred to as Figure S5) shows The Association betweenCortical Oscillations and Mood and Cognition in Source Space.

FIG. 12 (also referred to as Figure S6) shows The Association betweenChange in Complexity and Autobiographical Memory.

FIG. 13 (also referred to as Figure S7) shows The Association betweenChange in Complexity and Autobiographical Memory in Source Space.

FIG. 14 shows the Effect of Escitalopram on Complexity of TemporalDynamics.

FIG. 15 shows an Association between Modulation of Temporal Complexityand Mood.

FIG. 16 shows Escitalopram Induced Modulation of Complexity and ItsAssociation with Mood in Source Space.

FIG. 17 shows a Differential Early Changes in Complexity of TemporalDynamics during Escitalopram Treatment in Responders and Non-Responders.

FIG. 18 shows a Source Localization of Early Changes in TemporalComplexity in Non-Responders to Escitalopram.

FIG. 19 shows the Link Between Baseline Complexity and Change in Mood byEscitalopram.

FIG. 20 shows the Link between Baseline Complexity and Change in Mood byEscitalopram in Source Space.

FIG. 21 shows a Link between Baseline Complexity and Change in Mood byEscitalopram.

FIG. 22 shows a Link between Week 2 Complexity and Change in Mood byEscitalopram in Source Space.

DETAILED DESCRIPTION

We hypothesized that seizures impact both mood and cognition bymodifying the temporal complexity of brain signals in a time-scaledependent manner. We obtained resting-state EEG from two independentcohorts of patients undergoing either MST (n=15) or ECT (n=19).Depressive symptoms were rated through the Hamilton Rating Scale forDepressions (HAMD). General cognition and autobiographical memory wereobtained through the Montreal cognitive assessment scale (MoCA) andautobiographical memory interview (AMI) (Table 1).

Material and Methods

Patients.

A total of 34 subjects (age=46.0±14.0, 21 females) diagnosed withtreatment-resistant MDD participated in either of two parallelopen-label seizure therapy research protocols at Centre for Addition andMental Health (19 ECT and 15 MST). The demographic and clinicalcharacteristics are in Table 1.

TABLE 1 Demographics and Clinical Characterstics ECT ECT Non- MST MSTNon- Responders responders Responders responders [n = 12] [n = 7] [n =5] [n = 10] Age (years) 43.3 ± 16.3 57.7 ± 9.2 45.8 ± 8.01 40.1 ± 15.5Sex, M/F 4/8 2/5 2/3 5/5 Illness 19.9 ± 11.5  19.3 ± 13.6 25.4 ± 14.618.3 ± 14.2 Duration, [11] Years [n] Number of 12.5 ± 4.0  14.4 ± 3.218.6 ± 7.5  21.5 ± 5.8  Treatments Site of right unilateral rightunilateral Midline Frontal Midline Frontal Treatment, [n] ultra briefpulse ultra brief pulse [5] [10]  [12]  [4] bitemporal standard pulse(2) unilateral followed by bitemporal (1) Stimulation NA NA 100 Hz [4] 100 Hz [8]  Frequency, [n] 50 Hz [0] 50 Hz [2] 25 Hz [1] 25 Hz [0] %Change in 65.20 ± 7.8   13.5 ± 23.4 70.5 ± 16.0 13.6 ± 21.4 HAMD %Change in −13.0 ± 11.9   −7.69 ± 10.9 12.3 ± 24.2  0.9 ± 11.8 MoCA, [n][4] [2] [5] [8]plateaued (refer to Table 1 for number of treatments). Finally,methohexital was administered for sedation and succinylcholine asneuromuscular blocker. In general, the target dosage was 0.75 mg/kg ofmethohexital and 0.5 mg/kg of succinylcholine. MST was administered withMagpro MST using a Twin Coil (Magventure, Denmark). The centre of eachcircular coil was placed over F3 and F4 respectively, using the EEGinternational 10-20 system. This induces the

Seizure Therapy.

ECT was administered with MECTA spECTrum 5000Q (Corporation, LakeOswego, Oreg.) according to standards of practice (Sackeim et al.,2008). Sixteen patients received right unilateral ultra brief (RUL-UB)pulse width ECT, one received bitemporal (BT) brief pulse width ECT, andtwo started on RUL-UB and were switched to BT due to lack of efficacy(Table 1). Treatment sessions occurred twice or three times per week.Seizure threshold titration was used to determine stimulus intensity:RUL-UB was delivered at 6× threshold with a pulse width of 0.3 to 0.37msec and BT was delivered at 1.5× threshold with a pulse width of 1.0msec. ECT treatments were continued until depressive symptoms was inremission or improvement had highest electric field strength between thetwo coils roughly corresponding to Fz (Deng et al., 2013). Theorientation of the magnetic fields was posterior-anterior. Subjectsunderwent a dose titration procedure to establish convulsive stimulationthreshold. At 100 Hz and 50 Hz an initial train of 200 pulses was usedfollowed by increments of 200 pulses with a maximum train of 1000pulses. At 25 Hz an initial train of 100 pulses was used with incrementsof 100 pulses up to a maximum of 500 pulses. Twelve subjects received100 Hz, two subjects received 50 Hz and one subject received 25 Hz(Table 1). All stimulations occurred at the maximum stimulator output of100%. Threshold seizure was defined as a generalized tonic-clonicactivity ≥20 s of visual motor activity or ≥25 s of EEG seizureactivity. Subsequent treatments occurred three times per week, and wereinitially delivered with a train 400 pulses longer in the 100 Hz and 50Hz group and 200 pulses longer in the 25 Hz group. In subjects that hadnot achieved a 50% reduction in HAMD after three treatments, the doseincreased by 100 pulses (25 Hz), or 200 pulses (50 Hz, 100 Hz) up to amaximum of 500 or 1000 pulses, respectively. A maximum of 24 sessionswere allowed in the acute course. Methohexital (n=9), methohexital withremifentanil (n=5), and ketamine (n=1) were administered for sedationand succinylcholine was used as the neuromuscular blocker.

EEG.

Ten minutes of resting-state eyes closed EEG data were recorded withinone week prior to the start and within 48 hours after the completion ofa course of seizure therapy in both ECT and MST protocols. Subjects wereinstructed to sit in an armchair with eyes closed. EEG recording wasthrough a 64-channel NeuroScan EEG system. The reference electrode wasbehind CZ electrode, and ground was behind FZ. The sampling rate was 10kHz. The online filter setting was 0.05 to 1 kHz. The skin/electrodeimpedance was kept below 5 kOhm.

Mood.

Changes in depressive symptoms were assessed by HAMD within one weekprior to the start and within 48 hours after the completion of a courseof seizure therapy in both ECT and MST protocols. Response to treatmentwas defined as 50% change in HAMD from baseline.

Cognition.

19 patients (6 ECT and 13 MST) completed the MoCA within 48 hours priorto and within a week after a course of seizure therapy in bothprotocols. In addition, the autobiographical memory interview short form(AMI-SF) was completed in 12 MST patients before and after a course ofseizure therapy.

EEG Preprocessing.

Data were imported into MATLAB (The MathWorks, Inc. Natick, Mass. USA)for preprocessing. The open source signal processing functions in EEGLABtoolbox version 12.0 (Delorme and Makeig, 2004) were used for dataimport and preprocessing. The EEG signals were epoched into segments oftwo second duration and down sampled to 1 kHz. A notch filter(band-stop: 55-65 Hz) was used to remove the 60 Hz noise. EEG signalswere band passed filtered 1-50 Hz to further minimize contamination byhigh frequency artifact. The infinite impulse response (IIR) Butterworthfilter of second order and forward and backward filtering were appliedto maintain a zero phase shift. All epochs were manually reviewed andtrials and channels containing eye movements, muscle or any othernon-physiological artifact were discarded. The data was averagere-referenced.

Power.

The EEGLAB function spectopo was used to obtain the power spectrum foreach electrode. The relative power was obtained for 1 to 50 Hzfrequencies. Relative power was calculated as the ratio in the power ofeach frequency relative to the sum of power across all frequencies.

Multi-Scale Entropy.

MSE was examined across all electrodes using two steps (Costa et al.,2005): The coarse-graining process and the calculation of the sampleentropy (SampEn) for each coarse-grained time series. First, for a giventime series {x₁, x₂, . . . , x_(N)}, the multiple coarse-grained timeseries {y₁ ^((x)), y₂ ^((x)), . . . , y_(N) ^((x))} at scale factor τ(in this paper referred to as time scale) were calculated by averagingthe data points within non-overlapping windows of increasing length τ.Each element of the coarse-grained time series y₁ ^((τ)), was calculatedaccording to the equation:

$\begin{matrix}{y_{j}^{(\tau)} = {\frac{1}{\tau}{\sum\limits_{i = {{{({j - 1})}\tau} - 1}}^{j\;\tau}x_{i}}}} & (1)\end{matrix}$where τ represents the scale factor (i.e., time scale) and

$j\left( {1 \leq j \leq \frac{N}{\tau}} \right)$represents the time index of the element. The length of eachcoarse-grained time series was M, where

$M = {{floor}\mspace{11mu}{\left( \frac{N}{\tau} \right).}}$At scale factor (or time scale) τ=1, the coarse-grained time series wasthe original time series. Second, the degree of predictability wasmeasured for each of the multiple coarse-grained time series {y₁ ^((x)),y₂ ^((x)), . . . , y_(N) ^((x))} using SampleEn. SampleEn was calculatedaccording to the equation:SampleEn(r,m,M)=−ln(C(m+1)/C(m))  (2)where C(m) is the total number of pairs of m consecutive similar datapoints, C(m+1) is the total number of pairs of m+1 consecutive similardata points in the multiple coarse-grained time series. SampleEnquantifies the variability of time series by estimating thepredictability of amplitude patterns across a time series. In ourexperiments, two consecutive data points were used for data matching(i.e. m=2) and data points were considered to match if their absoluteamplitude difference was less than 15% (i.e., r=0.15) of standarddeviation of time series. MSE was calculated for a 30 second continuousepoch.

EEG Source Localization.

EEG source localization was performed using an open-source application,Brainstorm (Tadel et al., 2011). First, the electrode locations of our68-channel Neuroscan Quik Cap EEG electrode sites were co-registered tothe ICBM152 MRI template in Brainstorm. The forward solution was thencalculated using the OpenMEEG BEM head model (Gramfort et al., 2010) andthe inverse solution was derived using sLORETA (Pascual-Marqui, 2002),with the solution space constrained to the cortex surface. To localizethe dynamics of neural activity, we used the Destrieux Atlas, whichprovides 148 regions of interest (ROIs) in the MNI co-ordinate space(Destrieux et al., 2010). After the EEG data was mapped to the 148 ROIs,MSE and power spectrum measures were calculated for all subjects atthese sources.

Statistics

In addition to two intervention groups of ECT and MST, subjects weregrouped into two groups of antidepressant responders and non-responders;subjects were grouped as responders if there was a 50% or higher changein HAMD relative to baseline, and non-responders otherwise. Analysis ofvariance was used to 1) examine the effect of seizure therapy on MSE(1-70 time-scales) and relative power (1-50 Hz frequencies) for the maineffect of Seizure Therapy Intervention (ECT, MST) and Time (Pre, Post),as well as 2) Antidepressant Response (Responder, Non-Responder) andTime (Pre, Post) across 60 electrodes in sensor space and 148 ROIs insource space. Bootstrapping was used to correct for multiple comparisonsin the analysis of variance. For the post-hoc t-test comparisons,cluster-based non-parametric permutation test (Maris and Oostenveld,2007) was used to correct for the multiple comparisons in thismultidimensional dataset (60 channels (or 148 ROIs)×50 frequencies, 60channels (or 148 ROIs)×70 scales) by assigning significance statisticsto the probability of size of clusters formed by pooling adjacent pixelswith original test statistics p<0.05. The significance of originalclusters was defined against probably distribution of clusters obtainedthrough 1000 permutations of the shuffled data labels. Identicalparameters were used across the cluster-based permutations; thresholdstatistics of p<0.05, identical neighborhood, 1000 permutation usingMonte Carlo approach with cluster test statistics computed as themaximum of the cluster-level summed values. Analysis of variance, andpost-hoc paired t-test and independent sample t-test analyses were usedto calculate the original test statistics. Spearman correlationcoefficient was used to examine the association between change incomplexity and symptom severity or cognitive score. Similarly,cluster-based non-parametric permutation test was applied to thebehavioral scores to correct for the multiple comparisons in thecorrelation analyses.

In addition to correlation analysis, it was examined if change incomplexity classified patients based on antidepressant and cognitiveresponse. Subjects were grouped to have had cognitive decline if thepercent change in MoCA was negative. For AMI-SF, median performance wasused to divide the patients into two groups. The level of prediction wasquantified by the receiver operating characteristic (ROC) curve,plotting the sensitivity and specificity of the predictor (change incomplexity) across all possible threshold values. To determine thesignificance of the prediction, the area under the curve (AUC), standarderror of the AUC and confidence intervals were quantified for eachelectrode and source.

Throughout the paper, except otherwise noted, reported statistics arecorrected p values, and descriptive values indicate mean and standarddeviation unless otherwise stated. Percent change (i.e., %Δ) in outcomevariables is calculated as: (post treatment score−baselinescore/baseline score)×100, except for HAMD which is calculated as(baseline score−post treatment/baseline score)×100.

Results

The Impact of Seizure on Neural Oscillations

There was a significant (p<0.05) main effect of Intervention (df=72,mean F=14.7 (4.8 to 53.2)), Time (df=72, mean F=7.3 (4.8 to 19.9)) andIntervention×Time interaction effect (df=72, mean F=7.1 (4.3 to 18.4))across several frequencies and electrodes. There was also a significantmain effect of Antidepressant Response (mean F=5.10 (3.98 to 6.83)).Time (mean F=24.63 (4.07 to 80.16)), and Antidepressant Response×Timeinteraction effect (mean F=13.79 (4.02 to 61.86)) across multiple scalesand ROIs, however the main effect of Intervention or Intervention×Timeinteraction effect were not significant. Finally, there was a maineffect of Antidepressant Response (mean F=5.14 (3.61 to 9.58)), Time(mean F=27.42 (4.00 to 111.14)) and an interaction effect ofAntidepressant Response×Time (mean F=6.00 (3.96 to 14.53)) acrossmultiple scales and ROIs.

Post-hoc analyses replicated the findings of prior studies that ECTinduces an increase in relative power of slow cortical oscillations(Nobler and Sackeim, 2008). This effect was spatially global and presentregardless of the ECT therapeutic outcome. It was significant forfrequencies less than 8 Hz in responders (Figure S1 A) and was between 2to 7 Hz in non-responders (Figure S1 B). However, the slowing ofoscillations was not significant in MST (Figure S1 C-D). Consistently,we replicated the previous finding (Nobler and Sackeim, 2008) that thespatially global increase of slow oscillations (e.g., 1 Hz) isassociated with decline in general cognition (Figure S3 A). We found noassociation between change in slow oscillations and change in depressivesymptoms (Figure S2 A).

We discovered that common to ECT and MST responders there was a globalreduction in relative power of oscillations above 18 Hz (Figure S1 A,C). ECT non-responders also had a global decrease in oscillationsbetween 10 35 Hz (Figure S1 B). No changes were observed in MSTnon-responders (Figure S1 D). Comparing ECT with MST intervention group,we identified that ECT treatment led to higher increases in slowoscillations and higher decreases in high frequency oscillations (FigureS4A). This finding was also spatially global. Furthermore, comparingantidepressant responders with non-responders revealed that respondersexhibited higher reduction in the power of 22 Hz oscillations and alsohigher frequency oscillations (Figure S4B). This finding was spatiallyglobal at −22 Hz, but more local in higher frequencies (30-50 Hz).Specifically in 30-50 Hz, the reduction in power is observed in regionssuch as the inferior frontal sulcus, left orbital part of the frontalinferior gyrus, bilateral preocciptial notch, orbital gyri, lateralorbital sulcus, lateral occi-temporal sulcus, medial orbital sulcus,bilateral parieto-occipital sulcus, or bilateral superior parietallobule.

The results of correlation analysis revealed that the reduction in highfrequency oscillations (gamma, e.g., 43 Hz) was correlated withimprovement in depressive symptoms (Figure S2 A). This effect waslocalized to fronto-central (e.g. AF4, F1, FZ, F2, F4, FC2) andparieto-occipital (e.g., P7, P5, PO7, PO5, PO4, PO6, PO8, O1, OZ) brainregions in sensor space. In source space, there were significantnegative clusters in brain areas including the orbital sulci and gyri,bilateral posterior-dorsal part of the cingulate gyrus (dPCC), ventralPCC (vPCC), precuneus, parieto-occipital sulcus, occipital pole,inferior temporal gyrus, and lateral occi-temporal sulcus in frequencieshigher than 30 Hz (Figure S5 A).

Finally, a spatially widespread decrease in low frequency oscillation(<9 Hz) was correlated with a change in cognition (Figure S3 A). Sourceanalysis also revealed that this effect was spatially global. Finally,reduction in high frequency oscillations (e.g., >40 Hz) inparieto-central regions (e.g., C1, C3, CZ, CP3, P1, PZ, P2, P4, POZ) wascorrelated to change in cognition. In source space, this effect wasidentified primarily in brain regions including the central sulcus,angular gyrus, and subparietal sulcus (Figure S5 B).

The Impact of Seizure on Temporal Complexity

We then employed multi-scale entropy (MSE) (Costa et. al., 2005) toquantify the change in complexity of dynamics across multipletime-scales. In sensor space, there was a significant (p<0.05) maineffect of Intervention (df=72, mean F=10.5 (4.7 to 27.2)), Time (df=72,mean F=6.7 (4.6 to 14.4)) and Intervention×Time interaction effect(df=72, mean F=7.2 (4.5 to 18.6)) across multiple time-scales andelectrodes. There was also significant main effect of AntidepressantResponse (mean F=5.1 (4.5 to 6.8)), Time (mean F=13.7 (4.1 to 46.5)),and Antidepressant Response×Time interaction effect (mean F=6.3 (4.3 to11.2)). Similarly, in source space, we found a significant main effectof Intervention (mean F=14.84 (4.33 to 46.52)), Time (mean F=6.74 (4.20to 17.4)), and Intervention×Time interaction effect (mean F=5.76 (4.16to 11.20)) across multiple scales and ROIs. Finally, there was a maineffect of Antidepressant Response (mean F=5.59 (3.87 to 10.43)), Time(mean F=16.07 (4.02 to 61.19)), and interaction effect of AntidepressantResponse×Time (mean F=5.92 (4.04 to 17.09)) across multiple scales andROIs.

Post-hoc analysis revealed that, change in temporal complexity was onlysignificantly modified in responders of both seizure therapies. Commonto ECT and MST responders, there was a decrease in time-scales finerthan 20 factors (FIG. 1A, C). ECT responders showed a significant(cluster p=0.003) global decrease in time-scales less than 30 and asignificant (cluster p=0.002) global increase in coarser time-scales(spatially global changes are seen in time scales>50). Source-spaceanalysis (FIG. 5B) confirmed the spatially global extend of thisfinding. By contrast, in MST responders, a wide spread reduction intime-scales less than 20 was observed (cluster p=0.033). In MSTresponders, the reduction of MSE in fine time-scales (e.g., scale factor4) was found in the parieto-occipital (P1, P3, P2, P4, POZ, PO3, PO5,PO7, PO4, PO6, PO8, O1, OZ) and fronto-central regions (F4, FC1, FC2,FCZ, CZ, C1, CZ) in sensor space. Similarly, in source space, thischange was observed across several tempro-parieto-occipital regions(e.g., cuneous, precuneus, posterior-dorsal part of the cingulate gyrus(dPCC), parieto-occipital sulcus, occipital pole, etc) andfronto-central regions (e.g., opercular part of the inferior frontalgyrus, central sulcus, pre and post central gyrus, etc) (FIG. 5A). Nosignificant changes were observed in either ECT or MST non-responders(FIG. 1B, D).

Finally, comparing ECT with MST intervention group, we identified MSE infine time scales (e.g., <10) was significantly lower post treatment inECT compared to MST group, and increases in coarse time scales(e.g., >28) were significantly higher in ECT compared to MSTintervention group (FIG. 2A). This effect was spatially global.Comparing antidepressant responders with non-responders (FIG. 2B)identified significant differences between groups; however this findingdid not survive the cluster-based correction for multiple comparisons.The comparison revealed that responders may have a larger reduction inMSE post treatment at finer time scales in brain regions such as theprecuneus, bilateral cuncus, bilateral parieto-occiptial sulcus,bilateral occipital pole, bilateral lateral occi-temporal gyrus,calcarine sulcus, and bilateral posterior transverse collateral sulcus.Responders also appeared to have increased MSE post treatment in coarsertime scales (e.g., >40) mainly in the left inferior, middle and superiorfrontal sulcus, middle and superior frontal gyrus, and orbital part ofinferior frontal gyrus. This latter observation is likely related to thehigher number of ECT responders (compared to MST responders) whoexhibited significant increases in complexity of coarse time scales(e.g., FIG. 1A).

The Impact of Change in Temporal Complexity on Mood and Cognition

We then determined whether change in complexity was linked with theimpact of seizure on mood and cognition. We found a negative associationbetween percent changes in MSE (%ΔMSE) and percent change in HAMD(%ΔHAMD). This effect was selective to fine time-scales inparieto-occipital and fronto-central regions (FIG. 3A). Specifically, anegative association (p<0.01) was identified in tempro-parieto-occipital(TP7, P7, P5, P8, PO7, PO5, PO6, PO8, O1, O2, Oz) and fronto-centralregions (AF4, F1, FZ, F2, F4, FC1, FC2, FC4, FCZ, C1, C4, CZ) intime-scales less than 30. Source space analysis localized this effect ofseveral regions including the dPCC, cuneus, precuneus, parieto-occipitalsulcus, occipital pole, temporal sulci, and lateral occi-temporal sulcusas depicted in FIG. 5C. This association illustrated that a spatiallyspecific decrease in complexity of fine time-scales was linked with agreater improvement in depressive symptoms.

Moreover, we found a negative association between %ΔMSE and percentchange in general cognition (%ΔMoCA). This effect was spatially globalin coarse time-scales (e.g., >66) and included a wide range oftime-scales in parieto-central regions (e.g., PZ, POZ, P1, P2) (FIG.3B). Source-space analysis (FIG. 5D) confirmed that this effect wasspatially global across coarse time scales and included brain regionssuch as intraparietal sulcus and transverse parietal sulci. Thisnegative association was replicated for change in autobiographicalmemory (%ΔAMI) (Figure S6A) and was prominent in bilateralfronto-parietal brain regions. Likewise, source space analysis revealeda spatially global effect in coarse time scales with many brain areasinvolved including the bilateral superior parietal sulcus, and superiortemporal sulcus (Figure S7A). Collectively, this negative associationillustrated that an increase in MSE, in particular globally in coarsertime-scales, was linked with a greater decline in cognition.

These findings were region- and time-scale specific. That is, change incomplexity in occipital regions and fine time-scales was only associatedwith change in HAMD (e.g., O2 electrode, time-scale 4, r=−0.52,p=0.0017), and not change in MoCA (FIG. 3C), and change in complexity inparieto-central regions at coarser time-scale (e.g., PZ electrode,time-scale, 70, r=−0.63, p=0.0038) was only associated with change incognition, and not HAMD (FIG. 3D).

Classifying Antidepressant and Cognitive Response to Seizure Therapy

Finally, we examined whether change in temporal complexity couldclassify patients based on cognitive and antidepressant response toseizure therapy. We found that %ΔMSE classified the antidepressantresponse to seizure therapy with good performance and cognitive responsewith excellent performance as illustrated with the area under the curve(AUC) property of the receiver operating characteristic (ROC) curve(FIG. 4). Specifically, change in complexity of low time-scales (e.g.,4-6.8) in right parieto-occipital brain regions (OZ, O2, PO8) offeredgood (AUC≥0.8) prediction performance of antidepressant response ((e.g.,AUC (OZ electrode, time-scale 5)=0.83, p<0.0001; FIG. 4A, B)) and a fair(0.7<AUC<0.8) prediction performance was observed across low time-scales(e.g., 1-22) in bilateral fronto-central (e.g., FC1, FC2, FCZ, F1) andbilateral parieto-occipital (e.g., O1, PO3, PO5, PO7, PO4, PO6, P7, P8)brain regions. In source space, similar prediction accuracy wasidentified for the right occipital pole at similar time scale (AUC(right occipital pole, time-scale 5)=0.79, p<0.0001; FIG. 6A, B).Moreover, change in complexity of time-scales 14 and higher inparieto-central (e.g., PZ) and then globally in coarser time-scalesprovided excellent (e.g., AUC≥0.9) prediction performance for change incognition (e.g., AUC (P2 electrode, time-scale 23)=0.98, p<0.0001; FIG.4C, D). In source space, similar prediction accuracy was identified forthe intraparietal sulcus and transverse parietal sulci at similar timescale (AUC (intraparietal sulcus transverse parietal sulci, time-scale22)=0.97, p<0.00001; FIG. 6C, D).

These findings were region- and time-scale specific. That is, change incomplexity in occipital regions and fine time-scales (e.g., OZ,time-scale 5; FIG. 4B) only classified antidepressant response, and didpoorly in classifying cognitive response (AUC (OZ, time-scale 5)=0.55,p=0.35), and change in complexity in parieto-central regions at coarsertime-scale (e.g., P2, scale 23; FIG. 4D) only classified cognitiveresponse and did poorly in classifying antidepressant response (AUC (P2,time-scale 23)=0.47, p=0.63).

Moreover, the seizure therapy induced changes in autobiographical memory(%ΔAMI) could also be accurately (e.g., AUC range; 0.9 to 1.00; FigureS6B) classified by change in complexity in coarse time-scales(e.g., >47) in fronto-parietal regions (Figure S6B). Likewise, sourcespace analysis revealed a spatially global effect in coarse time scaleswith many in frontal parietal brain regions (Figure S7B). Finally, ourresults showed that change in complexity had better accuracy than neuraloscillations in predicting antidepressant or cognitive response toseizure therapy (Figures S2B, S3B).

Discussion

this study presented a novel biological target—i.e., complexity of thebrain resting-state dynamics—whose modulation in specific brain regionsexplained the antidepressant efficacy and cognitive consequences ofseizure therapy in depression. In contrast to neural oscillations,significant changes in the complexity of brain dynamics were onlypresent in responders of seizure therapy. Specifically, complexity offine time-scales was significantly reduced following successful ECT andMST. Across groups, the greater reduction in complexity of finetime-scales in fronto-central and parieto-occipital regions (e.g., rightoccipital pole) was associated with greater improvement of depressivesymptoms. In ECT, the complexity of coarse time-scales was alsosignificantly increased. Across groups, the greater global increase incomplexity of coarse time-scales was linked with the greater decline ingeneral cognition. Finally, region- and time-scale dependent changes incomplexity classified patients based on antidepressant efficacy (e.g.,in right occipital pole, scale 5) and cognitive consequences (e.g.,intraparietal sulcus and transverse parietal sulci, scale >22), ofseizure therapy with good (>80% and excellent (≥90%) accuracy,respectively.

ECT remains the most effective treatment in depression. Severalhypotheses have attempted to explain the mechanism of action of ECT(reviewed in (Farzan et al., 2014)). We recently proposed a unifyingconnectivity-resetting hypothesis, stating that ECT resets aberrantneural connectivity by activating the brain's major oscillatorypacemaker, thalamus and subsequently multiple thalamic loops (Farzan etal., 2014). The significant impact of seizure therapy on neuraloscillations has been quantified since early studies of ECT (reviewed in(Farzan et al., 2014)). The most replicated finding is the generalslowing of oscillations in ECT (Small et al., 1978) linked withimprovement in mood (Fink and Kahn, 1957; Sackeim et al., 1996). Whilewe replicated previous findings demonstrating that ECT induces increasein power of slow oscillations, this effect was present in both ECTresponders and non-responders. Moreover, we found no correlation betweenchange in slow oscillations and improvement in symptoms. Most previousstudies focused on limited and predefined frequency bands, using a fewelectrodes placed near the site of stimulation, or utilized statisticalapproaches that limited multi-dimensional analysis. The present studyused non-parametric statistical approaches and two distinct modalitiesof seizure induction to comprehensively assess changes common to seizuretherapy without a priori hypothesis or limiting analysis to regions andfrequencies of interest. Our comprehensive analysis revealed that it isthe reduction in relative power of frequencies 18 Hz and above,particularly higher than 35 Hz, rather than increase in slowoscillations, that is linked with response to seizure therapy across ECTand MST. The observation that successful MST modulated high frequencieswithout significantly impacting slow oscillations further confirms thatsuccessful seizure therapy may be achieved without impacting the slowoscillations that are linked with the adverse effects of ECT as reportedpreviously (Sackeim et al., 2000; Nobler and Sackeim, 2008) andreplicated in our study.

The peri-ictal characteristic of seizure reflects a rapid modificationof the brain dynamicity. It seems intuitive that modulation of the braindynamics would be a mechanism by which seizure exerts its therapeuticaction. Yet this has been only minimally investigated. In an ECT casestudy in three patients with depression, reduction in MSE in finetime-scales was reported (Okazaki et al., 2013). Our results are also inline with a previous study that showed an abnormal enhancement incomplexity of frontal brain regions in depression which was normalizedby antidepressant medication (Mendez et al., 2012). Complexity, asindexed by Lempel-Ziv Complexity, was increased as a function of age inhealthy subjects, a relationship not found in depression. Furthermore,six months of treatment with the antidepressant mirtazapine normalizedthe excess complexity in depression specifically in younger adults(Mendez et al., 2012). Such findings may suggest that both medicationsand seizure therapy act on reducing complexity in depression, while thehigher efficacy of seizure therapy may be linked to direct stimulationof oscillatory pacemakers. We found that antidepressant efficacy ofseizure therapy was linked with local changes in complexity. Ourfindings and these previous studies encourage design of non-seizureinterventions that target the same biological targets as seizure therapytoward eliminating the risk and complications of seizure induction.

Complexity of time series in biological systems is suggested to reflectplasticity to an ever changing environment and adaptability to stressors(McIntosh et al., 2014). When examined across brain regions andtime-scales, the complexity of brain dynamics can arise from transientincreases and decreases in correlated activity across brain regionsreflecting rate of information generation (McIntosh et al., 2014).Induction of seizure could reset integration and synchronization ofinformation across brain regions, through activation of thalamus andmultiple thalamic loops and interconnected brain regions, significantlyimpacting rate of information generation across distributed brainnetworks. The association between reduction in complexity andimprovement in symptoms is in line with imaging findings that have shownthat depression is associated with states of hyperconnectivity betweenfrontoparietal and default mode network (Kaiser and Pizzagalli, 2015).The clinically relevant reduction of complexity in fronto-central andparieto-occipital regions adds to the resting-state functionalconnectivity findings in fMRI literature.

A recent study using fMRI data from Human Connectome Project showeddifferential association between functional connectivity ofresting-state networks and complexity of fMRI time signals in fineversus coarse time-scales (McDonough and Nashiro, 2014). The time-scalesin this fMRI study are coarse in comparison to the present highresolution EEG study, hindering direct interpretation. Yet, it providesevidence that there may be a link between seizure-induced changes incomplexity and aberrant neural connectivity in depression. We suggestthat a change in dynamics of functional connectivity between distributedbrain regions may be a mechanism by which seizure therapy exerts itsimpact on behaviour. Design of non-invasive interventions that canselectively modify the complexity of the brain dynamics will enablecareful examination of the consequence of region- and network-specificmodification of MSE on human behavior.

Moreover, the finding that seizure induced changes in the occipital lobe(e.g., occipital pole) were linked to mood improvement and predictedtherapeutic response is also in line with several lines of emergingevidence that have linked depression with impairment in this brainregion (reviewed in (Koch and Schultz, 2014)). For example, as reviewedby Koch, et al., a recent meta-analysis reported the right occipitallobe, with the inferior fronto-occipital fibre tract, to be among themost consistently reported site of decreased white matter integrity inthis population. Furthermore, in addition to the changes in white matterstructure, changes in resting-state connectivity and gray matter volumehave been previously shown in this brain region in depression (e.g.,(Grieve et al., 2013; Meng et al., 2014)). Moreover, a recent study hasreported that occipital bending is more common in depression (Maller etal., 2015). Finally, a prior study in post-stroke depression haveidentified that post-stroke depression was closely linked with the righthemisphere lesion volume and its proximity to the occipital pole(Shimoda and Robinson, 1999). Therefore, our finding that seizuretherapy may exert its antidepressant efficacy by impacting the dynamicsof the occipital region, particularly source localized to the occipitalpole, not only complements these prior findings, but also provides adirection for development of novel antidepressant treatments.

This study also adds new insight about the link between region- andtime-scale dependent changes of complexity and human behavior. Weillustrated a region-specific reduction of MSE in fine time-scales thatwas linked with improvement in mood, and a more spatially-distributed(e.g., bilateral fronto-parietal) increase of MSE in coarse time-scalesthat was linked with cognitive decline. Previous studies have reportedboth global and region-specific modulation in complexity, such as duringdevelopment (Misic et al., 2010) and aging (McIntosh et al., 2014),respectively. Indeed, the observed link between increase in MSE incoarse time-scales and cognitive decline is consistent with findings inAlzheimer's disease (Mizuno et al., 2010). Our findings also extendprevious studies that revealed significant modifications in this markerduring adolescence (Vakorin et al., 2013), when the prevalence rate ofdepression peaks, and in disorders of cognition and affect withoverlapping symptoms with depression including autism spectrum disorder(Bosl et al., 2011) or schizophrenia (Takahashi et al., 2010) in whichseizure therapy is also indicated.

MST treatment frequency may be an important dimension involved inproduction of a seizure. The majority of prior MST trials have appliedMST at 100 Hz frequency to achieve seizure induction. However, it wasproposed that the optimal frequency for seizure induction may be in thevicinity of 22 Hz (Peterchev et al., 2010). In our sample, the mostcommon MST frequency used was 100 Hz (in 12/15 subjects), while a fewpatients who also took part in the resting-state EEG assessmentsreceived lower frequency of stimulation to induce seizure. Nevertheless,the present EEG study was not designed to evaluate the impact ofdifferent frequency of stimulation on therapeutic outcome. We proposethat the markers presented in this study have the potential to be usedto protect against any potential cognitive adverse effects throughneurophysiological monitoring that may predate any cognitivedeterioration.

Finally, our findings support a focal antidepressant target for seizuretherapy. First, the association between change in MSE and depressivesymptoms was identified in fronto-central and parieto-occipitalelectrodes and source localized to several parieto-occipital brainregions including the occipital pole. Second, the reduction in MSE wasobserved more localized to these brain regions in fine time-scales inresponders in MST which is a more focal method of seizure induction.Consistently, the association between change in neural oscillations anddepressive symptoms was also localized to fronto-central andparieto-occipital brain regions and high frequency oscillations thatcorrespond to fine time-scales. Fourth, the classification performanceof the change in complexity was region- and time-scale specific. Brainregions at which change in complexity classified antidepressant responsewith good accuracy failed to classify cognitive response, and brainregions at which change in complexity classified cognitive responsefailed to classify antidepressant response. Recent evidence indicatesthe possibility of modulating the temporal complexity of brain signalsby network guided rTMS (Farzan et al., 2016). Therefore, treatment ofdepression may benefit from design of more localized seizure inductionstrategies or non-seizure treatments (e.g., rTMS) that could focallymodulate complexity.

Example 1: Escitalopram (Non-Seizure, Pharmacological Antidepressant)Brain Temporal Complexity Throughout 8 Weeks of Escitalopram Therapy

Material and Methods

95 participants were included in this study. Data were collected as partof the CAN-BIND 1 project (Lam et al., 2016). Participants wereoutpatients aged 18-60 years of age, and met DSM-IV-TR (2000) criteriafor major depressive episode (MDE) in MDD, confirmed by the MiniInternational Neuropsychiatric Inventory (MINI) (Sheehan et al., 1998).Data were gathered across four study sites: University Health Network,Centre for Addiction and Mental Health in Toronto, Queen's University inKingston, Ontario, Canada, and University of British Columbia inVancouver, British Columbia, Canada. Study procedures were approved byresearch ethics institutional review boards at each site. Allparticipants signed written informed consent prior to participation. Atstudy enrollment, all participants were experiencing a MDE duration ≥3months with a Montgomery Asberg Depression Rating Scale (MADRS) score≥24. All participants were free of psychotropic medications for at least5 half-lives before baseline Visit 1. Participants were excluded if theyhad: 1) any Axis I diagnosis other than MDD, that was considered theprimary diagnosis; 2) diagnosis of Bipolar Disorder Type I or II; 3) asignificant Axis II diagnosis (borderline, antisocial); 4) high suicidalrisk, 5) substance dependence/abuse in the past 6 months; 6) presence ofsignificant neurological disorders, 7) head trauma, or 8) other unstablemedical condition. Exclusionary criteria related to the medicationsincluded: having failed four or more adequate pharmacologicalinterventions, having started psychological treatment within the past 3months with the intent of continuing the treatment, previously havingfailed escitalopram treatment or showing intolerance to escitalopram,and being at risk for hypomanic switch (i.e. with a history ofantidepressant induced hypomania). In addition, female participants whowere pregnant or breastfeeding were excluded. Finally, participants wereexcluded from this study if they were lost to attrition before studybaseline, discontinued in the middle of the treatment, or did notcomplete all EEG and clinical assessment visits.

Clinical Measures

The study period was eight weeks and participants were assessed withMADRS every 2 weeks starting from before administration of studymedication (baseline). Response was defined as a ≥50% decrease in MADRSscore from baseline to week 8.

Treatment Trial

Treatment was administered in an open-label manner. Escitalopram dosingwas started at 10 mg daily and increased to 20 mg daily at week 2 orlater if clinically necessary. The dose could be reduced to 10 mg at thediscretion of the treating psychiatrist if patients were unable totolerate the 20 mg dose.

EEG Recording

Subjects were instructed to sit quietly in a testing room, while 8minute of resting-state eyes closed EEG were recorded. Subjects wereinstructed to remain still, reduce eye blinks or movement to a minimum,and refrain from falling asleep. Four EEG acquisition systems were usedacross study sites as described in details elsewhere (see Baskaran etal., 2017, and Farzan et al., 2017).

EEG Preprocessing. Data were imported into MATLAB (The MathWorks. Inc.Natick, Mass., USA) for preprocessing. The open source EEGLAB toolboxversion 12.0 (Delorme and Makeig, 2004) were used for data import. TheEEG signals were epoched into segments of two seconds duration. Then astandardized custom-made and stream-lined open source software developby our team, EEGERP toolbox (http://www.tmseeg.com./multisiteprojects),was used to preprocess the data in 6 steps as described elsewhere(Farzan et al., 2017). All data were brought down to sampling rate of512 Hz for consistency across all sites, and average re-referenced inthe final preprocessing step.

Power.

The EEGLAB function spectopo was used to obtain the power spectrum foreach electrode. The relative power was obtained for 1 to 50 Hzfrequencies. Relative power was calculated as the ratio in the power ofeach frequency relative to the sum of power across all frequencies.

Multi-Scale Entropy.

MSE was examined identical to a prior publication (Farzan et al., Brain2016). Identical to this prior publication: “MSE was obtained across allelectrodes using two steps (Costa et al., 2005): The coarse-grainingprocess and the calculation of the sample entropy (SampEn) for eachcoarse-grained time series. First, for a given time series {x₁, x₂, . .. x_(N)}, the multiple coarse-grained time series {y₁ ^((τ)), y₂ ^((τ)),. . . y_(N) ^((τ))} at scale factor τ were calculated by averaging thedata points within non-overlapping windows of increasing length τ. Eachelement of the coarse-grained time series y_(j) ^((τ)), was calculatedaccording to the equation:

$\begin{matrix}{y_{j}^{(\tau)} = {\frac{1}{\tau}{\sum\limits_{i = {{{j{({j - 1})}}\tau} - 1}}^{j\;\tau}x_{i}}}} & (1)\end{matrix}$

where τ represents the scale factor and

$j\left( {1 \leq j \leq \frac{N}{\tau}} \right)$represents the time index of the element.

The length of each coarse-grained time series was M, where

$M = {{floor}\mspace{11mu}{\left( \frac{N}{\tau} \right).}}$At scale factor τ=1, the coarse-grained time series was the originaltime series. Second, the degree of predictability was measured for eachof the multiple coarse-grained time series {y₁ ^((τ)), y₂ ^((τ)), . . ., y_(N) ^((τ))} using SampleEn. SampleEn was calculated according to theequation:SampleEn(r,m,M)=−ln(C(m+1)/C(m))  (2)where C(m) is the total number of pairs of m consecutive similar datapoints, C(m+1) is the total number of pairs of m+1 consecutive similardata points in the multiple coarse-grained time series. SampleEnquantifies the variability of time series by estimating thepredictability of amplitude patterns across a time series. In ourexperiments, two consecutive data points were used for data matching(i.e. m=2) and data points were considered to match if their absoluteamplitude difference was less than 15% (i.e., r=0.15) of standarddeviation of time series. MSE was calculated for a 30 second continuousepoch.”

EEG Source Localization.

We used the Destrieux Atlas available as a part of an open-sourceapplication, Brainstorm (Destrieux et al., 2010; Tadel et al., 2011), tolocalize the dynamics of neural activity. The Destrieux atlas provides148 Region of Interests (ROIs) in the MNI co-ordinate space. Sourceestimation was performed using sLORETA (Pascual-Marqui, 2002) asimplemented in Brainstorm. Source reconstruction was constrained to thecortex surface of the OpenMEEG BEM head model (Gramfort et al., 2010).After the data was mapped to 148 ROIs in the source space, MSE and powerspectrum measures were calculated for all subjects at these sources.

Statistics

Subjects were grouped into two groups of antidepressant responders andnon-responders according to previous literature: subjects were groupedas responders if there was a 50% or higher change in MADRS at week 8relative to baseline, and non-responders otherwise. Analysis of variancewas used to 1) examine the effect of medication on MSE (1-70time-scales) and relative power (1-50 Hz frequencies) for the maineffect of Time (Baseline, week 2, week 8), as well as 2) AntidepressantResponse (Responder, Non-Responder) and Time (Baseline, week 2, week 8)across 58 electrodes in sensor space and 148 ROIs in source space.Cluster-based non-parametric permutation test (Maris and Oostenveld,2007) was used to correct for the multiple comparisons in thismultidimensional dataset (58 channels (or 148 ROIs)×50 frequencies, 58channels×70 scales) by assigning significance statistics to theprobability of size of clusters formed by pooling adjacent pixels withoriginal test statistics p<0.05. Identical parameters were used in thecluster-based permutations: threshold statistics of p<0.05, identicalneighborhood matrix, 1000 permutation using Monte Carlo approach withcluster test statistics computed as the maximum of the cluster-levelsummed values. Analysis of variance and post-hoc paired t-test andindependent sample t-test analyses were used to calculate the originaltest statistics. The significance of original clusters was definedagainst probably distribution of clusters obtained through 1000permutations of the shuffled data labels. Spearman correlationcoefficient was used to examine the association between change incomplexity and symptom severity. Similarly, cluster-based non-parametricpermutation test was applied to MADRS scores to correct for the multiplecomparisons in the correlation analyses.

In addition to correlation analysis, it was examined if change incomplexity classified patients based on antidepressant response. Thelevel of prediction was quantified by the receiver operatingcharacteristic (ROC) curve, plotting the sensitivity and specificity ofthe predictor (change in complexity) across all possible thresholdvalues. To determine the significance of the prediction, the area underthe curve (AUC), standard error of the AUC and confidence intervals werequantified for each electrode.

Throughout the paper, except otherwise noted, reported statistics arecorrected p values, and descriptive values indicate mean and standarddeviation unless otherwise stated. Percent change (i.e., %Δ) in outcomevariables is calculated as: (post treatment score−baselinescore/baseline score)×100, except for MADRS which is calculated as(baseline score−post treatment)×100.

Results

TABLE 2 All Groups Responders Non-responders (n = 95) (n = 45) (n = 50)Age (mean +/− std)   36 +/− 12.4 36.2 +/− 12.7 35.8 +/− 12.2 Gender(F/M) (n) 63/32 30/15 33/17 MADRS Baseline 30.0 +/− 6.09 29.9 +/− 6.1530.2 +/− 6.10 (mean +/− std) MADRS Post Week 8 15.4 +/− 9.7  7.62 +/−5.24 22.3 +/− 7.18 (mean +/− std)

FIG. 14 shows effect of Escitalopram on Complexity of Temporal Dynamics.Top: Images show the original post-hoc test statistics comparing MSE pre(baseline) to post treatment (week 8) across all electrodes (1 to 58)and all time-scales (1 to 70) (blue: increases; red: decreases followingtreatment) for responders (left) and non-responders to escitalopram(right). Bottom. Each topography reflects the significant t-mapsfollowing correction for multiple comparison, using cluster-basednon-parametric permutation test for p=0.05, depicting only the clustersp<0.09 and setting to 0 other pixels. Topographies highlight the spatialcharacteristics of the reduction of MSE in fine time-scales inresponders to escitalopram in fronto-parietal brain regions (clusterp=0.09). No significant changes were observed in non-responders.

FIG. 15 shows association between Modulation of Temporal Complexity andMood. Top. Image illustrate all the significant (original p <0.05)spearman correlation coefficients (rho) between percent change in MADRSand change in MSE (pre-post) in 95 patients receiving escitalopramtherapy. Cluster-based correction for multiple comparison resulted insignificant clusters (p<0.05) in parieto-occipital and fronto-centralregions in time-scale less than 10 factors. Bottom. Topographiesillustrate spatial distribution of this association.

FIG. 16 shows Escitalopram Induced Modulation of Complexity and ItsAssociation with Mood in Source Space. In left image, x-axis representsthe time scales (1 to 70) and y-axis represents Regions of Interest(ROIs) of the Destrieux Atlas (1 to 148). Images show the post-hoc teststatistics following cluster-based permutation test correction formultiple comparison, depicting only the significant clusters p<0.05,labeling only the significant corresponding ROIs and setting to 0non-significant pixels. Scatter plots show that only region-specificreduction in MSE in fine time-scales (less than 20 time scales) wassignificantly associated with enhancement of MADRS.

FIG. 17 shows differential Early Changes in Complexity of TemporalDynamics during Escitalopram Treatment in Responders and Non-Responders.Top. Images show the original post-hoc test statistics comparing MSE atweek 2 relative to pre treatment (baseline) across all electrodes (1 to58) and all time-scales (1 to 70) (blue: decreases; red: increasesfollowing treatment) for responders (Left column) and non-responders(Right column) from baseline to week 2. Bottom. Each topography reflectsthe significant t-maps following correction for multiple comparison,using cluster-based non-parametric permutation test, depicting only thesignificant clusters p<0.05 and setting to 0 non-significant pixels.Topographies highlight the spatial characteristics of the reduction ofMSE in coarse time-scales in non-responders. The reduction of MSE incoarse time-scales (e.g., scale factor >18) was localized to lateralfrontal, fronto-temporal, and parieto-occipital brain regions. Nosignificant changes were observed in non-responders.

FIG. 18 shows source Localization of Early Changes in TemporalComplexity in Non-Responders to Escitalopram. X-axis represents the timescales (1 to 70) and y-axis represents Regions of Interest (ROIs) of theDestrieux Atlas (1 to 148). Images show the post-hoc test statisticsfollowing cluster-based permutation test correction for multiplecomparison, depicting only the significant clusters p<0.05, labelingonly the significant corresponding ROIs and setting to 0 non-significantpixels.

FIG. 19 shows link Between Baseline Complexity and Change in Mood byEscitalopram. Top. Image illustrate all the significant (p<0.05)spearman correlation coefficients (rho) between percent change in MADRS(week 8 to baseline) and baseline MSE (pre-post) in 95 patientsreceiving escitalopram therapy. Cluster-based correction for multiplecomparison resulted in significant clusters (p<0.05) across multiplebrain regions (globall) such as in parieto-occipital and fronto-centralregions in time-scale higher than 37, Bottom. Topographies illustratespatial distribution of this association.

FIG. 20 shows link between Baseline Complexity and Change in Mood byEscitalopram in Source Space. Source analysis of data from prior figurereflects all brain region whose baseline complexity is associated withchange in depressive symptoms following Escitalopram therapy.

FIG. 21 shows link between Week 2 Complexity and Change in Mood byEscitalopram. Image illustrate all the significant (p<0.05) spearmancorrelation coefficients (rho) between percent change in MADRS (week 8to baseline) and week 2 MSE in 95 patients receiving escitalopramtherapy. Cluster-based correction for multiple comparison resulted insignificant clusters (p<0.05) across multiple brain regions. Bottom.Topographies illustrate spatial distribution of this association.

FIG. 22 shows link between Week 2 Complexity and Change in Mood byEscitalopram in Source Space. Source analysis of data from prior figurereflects all brain region whose complexity at week 2 of treatment isassociated with change in depressive symptoms following Escitalopram 8weeks of therapy.

FIG. 1 shows the Effect of Seizure Therapy on Complexity of TemporalDynamics. Top. Waveforms depict multi-scale entropy (MSE) pre (blackline) and post (red line) electroconvulsive therapy (ECT) and magneticseizure therapy (MST) in responders (A, C) and non-responders (B, D).The lines represent the average MSE (y-axes) across electrodes (dots)for time-scales 1 to 70 (s-axes). Middle. Images show the originalpost-hoc test statistics comparing MSE post to pre-treatment across allelectrodes (1 to 60) and all time-scales (1 to 70) (blue: decreases;red: increases following treatment) for responders and non-responders toECT (A; B) and MST (C, D). Bottom. Each topography reflects thesignificant t-maps following correction for multiple comparison, usingcluster-based non-parametric permutation test, depicting only thesignificant clusters p<0.05 and setting to 0 non-significant pixels.Topographies highlight the spatial characteristics of the reduction ofMSE in fine time-scales common to both ECT and MST responders (A, C) andthe increase in MSE in coarse time-scales following ECT alone (A). InECT responders, there was a significant cluster p=0.003) global decreasein time-scales less than 30 and a significant (cluster p=0.002) globalincrease in coarser time-scales. By contrast, in MST responders, only awide spread reduction in time-scales less than 20 was observed (clusterp=0.033). In MST responders, the reduction of MSE in fine time-scales(e.g., scale factor 4) was localized to parieto-occipital (P1, P3, P2,P4, POZ, PO3, PO5, PO7, PO4, PO6, PO8, O1, OZ) and fronto-centralregions (F4, FC1, FC2, FCZ, CZ, C1, CZ). No significant changes wereobserved in either ECT or MST non-responders.

FIG. 2. depicts the Effect of Seizure Therapy on Complexity in theSource Space. In all images, X-axis represents the time scale (1 to 70)and y-axis represents Regions of Interest (ROIs) of the Destrieux Atlas(1 to 148). The ROIs are grouped into brain regions in the left (L: theupper half of the images) and right (R: the lower half of the images)hemisphere separated by the horizontal black line. A. Image show thepost-hoc independent sample t-test statistics following cluster-basedpermutation test correction for multiple comparison, depicting only thesignificant clusters p<0.05, labeling only the significant correspondingROIs, and setting to 0 non-significant pixels. Image shows the t-teststatistics comparing the changes in MSE (Post-Pre/Pre) betweenparticipants who received ECT and MST interventions (red: higherincreases in ECT; blue: higher decreases in ECT). This image depict thatMSE in fine time scales (e.g., <10) was significantly lower posttreatment in ECT compared to MST group, and increases in coarse timescales (e.g., >28) were significantly higher in ECT compared to MSTintervention group. B. Image shows the independent sample t-teststatistics comparing the change in MISE (Post-Pre/Pre) betweenparticipants who were considered responders to seizure therapy (>=50%reduction in HAMD from baseline) and non-responders (red: higherincreases in responders; blue: higher decreases in responders). Theregions of significance did not survive the cluster-based correction formultiple comparisons at cluster p<0.05, thereby, this image depicts theoutcome of bootstrapping statistics only. Responders may have morereduction is MSE post treatment in fine time scales in brain regionssuch as precuneus, bilateral cuneus, bilateral parieto-occipital sulcus,bilateral occipital pole, bilateral lateral occi-temporal gyms,calcarine sulcus, and bilateral posterior transverse collateral sulcus.Responders may also have more increases post treatment in coarser timescales (e.g., >40) mainly in the left inferior, middle and superiorfrontal sulcus, middle and superior frontal gyrus, and orbital part ofinferior frontal gyms.

FIG. 3 shows the Association between Modulation of Temporal Complexityand Mood and Cognition. A. Topographies illustrate all the significant(original p<0.05) spearman correlation coefficients (rho) betweenpercent change in HAMD and MSE in 34 patients receiving seizure therapy.Cluster-based correction for multiple comparison resulted in significantnegative clusters (p<0.01) in parieto-occipital (TP7, P7, P5, P8, PO7,PO5, PO6, PO8, O1, O2, Oz) and fronto-central regions (AF4, F1, FZ, F2,F4, FC1, FC2, FC4, FCZ, C1, C4, CZ) in time-scale less than 30 factors.B. Topographies illustrate all the significant (original p<0.05)spearman correlation coefficients (rho) between the percentage change inMoCA and MSE across time-scales in 19 patients receiving seizuretherapy. Cluster-based correction for multiple comparison revealed asignificant negative cluster (p<0.01) in parieto-central region (e.g.,PZ, POZ, P1, P2) across time-scales and globally in courser (higher)time-scales. C, D. Scatter plots highlight the time-scale andregion-specific association between percent change in MSE (y-axes) andpercent change in HAMD (x-axis in C), and percent change in MoCA (x-axisin D). C. Scatter plots show that change in MSE was significantlyassociated with change in HAMD in the occipital region in finetime-scale (O2, time-scale 4, r=−0.52, p=0.0017) but not coursetimes-scale (O2, time-scale 70, r=0.07, p=0.71). D. Scatter plots showthat change in MSE was significantly associated with change in MoCA inthe parieto-central region in course time-scale (PZ, time-scale 70,r=0.63, p=0.0038) but not fine time-scales (PZ, time-scale 4, r=−0.28,p=0.24).

FIG. 4. shows the Region-Specific Change in Temporal Complexity PredictsChange in Mood and Cognition. A, C. Topographies depict area under thecurve (AUC) of the receiver operating characteristic (ROC) curve ofchange in multiscale entropy (MSE) in predicting antidepressant (A), andcognitive change (C) in response to seizure therapy at every electrodeand time-scale. The hot colors illustrate higher AUC and betterprediction. Change in complexity of low time-scales (e.g. 4-6,8) inright parieto-occipital brain regions (OZ, O2, PO8) offered good (AUC0.8) prediction performance of antidepressant response and a fair(0.7<AUC <0.8) prediction performance was observed across lowtime-scales (e.g., 1-22) in bilateral fronto-central FC1, FC2, FCZ, F1)and bilateral parieto-occipital (e.g., O1, PO3, PO5, PO7, PO4, PO6, P7,P8) brain regions (A). Change in complexity of time-scales 14 and higherin parieto-central (e.g., PZ and then globally in coarser time-scalesprovided excellent (e.g., AUC 0.9) prediction performance for change incognition. B, D. Figures depict the ROC curve across all possiblethreshold values of the predictor for an electrode and time-scale withbest prediction performance for antidepressant response (OZ, scale 5)(B) and change in cognition (e.g., AUC (P2 electrode, time-scale23)=0.98 p<0.0001). (D) X-axes represent false positive rates(I-specificity), y-axes the true positive values (sensitivity). The redcircle shows the optimum operating point of the ROC curve. B. At optimumpoint, this electrode and scale has 82% sensitivity and 77% specificity(good classification). D. At optimum point, this electrode and scale has89% sensitivity and 100% specificity (excellent classification).

FIG. 5 shows a Seizure induced Modulation of Complexity and. ItsAssociation with Mood and Cognition in the Source Space. In all images,X-axis represents the time scales (1 to 70) and y-axis representsRegions of Interest (ROIs) of the Destrieux Atlas (1 to 148). The ROIsare grouped into left (L: the upper half of the images) and right (R:the lower half of the images) hemisphere brain regions separated by thehorizontal red line in each figure. Images show the post-hoc teststatistics following cluster-based permutation test correction formultiple comparison, depicting on the significant cluster p0.05,labeling only the significant corresponding ROIs and setting to 0non-significant pixels. Top: Images show the t-test statistics comparingMSE post to pre-treatment (blue: decreases; red: increased followingtreatment). A. In MST responders, a wide spread reduction in time-scalesless than 20 was observed (cluster p<0.05). B. By contrast, in ECTresponders, there was significant (cluster p 0.01) global decrease intime-scales less than 30 and significant (cluster p<0.01) globalincrease in coarser time-scales. In MST responders, the reduction of MSEin fine time-scales was found in several tempro-parieto-occipitalcuneous, precuneus, posterior-dorsal part of the cingulate gyrus (dPCC),parieto-occipital sulcus, occipital pole, etc) and fronto-central brainregions (e.g., opercular part of the inferior frontal gyms, centralsulcus, pre and post central gyrus, etc). No significant changes wereobserved in either ECT or MST non-responders. Bottom: C. Imageillustrate the significance (p<0.05) spearman correlation coefficients(rho) between percent change in HAMD and MSE in 34 patients receivingseizure therapy. There was significant negative clusters (p<0.01) intime-scale less than 20 factors in tempro-parieto-occipital regionsincluding the bilateral dPCC, bilateral vPCC, bilateral cuneus,precuneus, parieto-occipital sulcus, occipital pole, temporal sulci,bilateral inferior temporal sulcus, bilateral lateral occi-temporalsulcus, bilateral calcarine sulcus, bilateral anterior and posteriortransverse collateral sulcus. D. Image illustrates spearman correlationcoefficients (rho) between percent change to MoCA and MSE acrosstime-scales in 19 patients receiving seizure therapy. There wassignificant clusters (p<0.005) in several central, parieto-centra,parieto-occipital, occi-temporal, and temporal brain regions (as labeledon the image) across primarily coarser (>30) time-scales.

FIG. 6 shows the Prediction of Change in Mood and Cognition in theSource Space. Images depict area under the curve (AUC) of the receiveroperating characteristic (ROC) curve of change in multi scale entropy(MSE) in predicting antidepressant (A), and cognitive change (C) inresponse to seizure therapy at every region of interest (ROI) of theDestrieux Atlas (1 to 148) and each time-scale (1 to 70). Hot colorsillustrate higher AUC and better prediction. Change in complexity of lowtime-scales (1 to 20) in parieto-occipital regions (e.g.,parieto-occipital sulcus, occipital pole, calcarine sulcus) offeredmoderate to good (e.g., AUC of 0.75 to 0.80) prediction performance forchange in antidepressant response (A). Change in complexity of highertime-scales in parietal brain regions and then spatially globally acrosstime-scales provided excellent >0.9) prediction performance for changein cognition. B, D. Figures depict the ROC curve across all possiblethreshold values of the predicator for an ROI and time-scale forantidepressant response (AUC (right occipital pole, time scale 5)=0.79,p<0.0001) (B) and change in cognition (e.g., AUC (intra andtrans-parietal sulcus, time-scale 22) −0.97, p<0.0001). (D). X-axesrepresent false positive rates (1-specificy), y-axes the true positivevalues (sensitivity). The red circle shows the optimum operating pointof the ROC curve. B. At optimum point, this brain region and scale has70% sensitivity and 94% specificity. D. At optimum point, this ROI andscale has 100% sensitivity and 90% specificity.

FIG. 7 (also referred to as Figure S1). Effect of Seizure Therapy onCortical Oscillations. Top. Waveforms depict the relative power spectrumof resting-state eyes-closed EEG pre (black waveforms) and post (redwaveforms) electroconvulsive therapy (ECT) and magnetic seizure therapy(MST) in responders (A, C) and non-responders (B, D). The x-axes arefrequency in Hz and the y-axes the relative power in dB. Middle. Imagesshow the original post-hoc test statistics maps comparing the relativepower across frequency bands (x-axes) and channels (y-axes) postcompared to pre-treatment (blue: decreases; red: increases followingtreatment) for responders and non-responders, Bottom. Each topographyreflects the significant t-map depicting only the significant clustersp<0.05, setting to 0 non-significant pixels. Topographies highlight thespatial characteristics of a global increase in relative power offrequencies <8 Hz (cluster p=0.018) and a significant (cluster p<0.001)global decrease in frequencies >9 Hz in ECT responders, but a widespread reduction in relative power of frequencies >18 Hz (clusterp<0.001) in MST Responders. Significant (cluster p=0.042) but lesspronounced wide spread increase of 2 to 7 Hz and decrease (clusterp=0.017) of 10 to 35 HZ were observed in ECT non-responders, Nosignificant changes were observed in MST non-responders,

FIG. 8 (also referred to as Figure S2) shows The Association betweenCortical Oscillations and Mood. A. Topographies illustrate all thesignificant (original p<0.05) spearman correlation coefficients (rho)between percent change in HAMD and change in power. All electrodes andfrequencies that did not survive the correction for multiple comparisonswere set to 0 (green colors). Cluster-based permutation test correctionfor multiple comparison revealed significant negative clusters (p<0.01)in high frequencies (e.g., >30 Hz) in parieto-occipital regions (e.g.,P7, P5, PO7, PO5, PO4, PO6, PO8, OI, OZ) and fronto-central regions(e.g., AF4, F1, FZ, F2, F4, FC2) B. Topographies depict area under thecurve (AUC) of the receiver operating characteristic (ROC) curve ofchange in relative power of cortical oscillations in predicting changein depressive symptoms in response to seizure therapy at every electrodeand frequency. The hot colors illustrate higher AUC and betterprediction. Change in cortical oscillations did not provide goodaccuracy (i.e., AUC >0.8) in predicting change in depressive symptoms.

FIG. 9 (also referred to as Figure S3) shows The Association betweenCortical Oscillations and Cognition. A. Topographies illustrate all thesignificant (original p<0.05) spearman correlation coefficients (rho)between percent change in MoCA and change in power. All electrodes andfrequencies that did not survive the correction for multiple comparisonswere set to 0 (green colors). Cluster-based non-parametric correctionfor multiple comparison revealed a significant global negative cluster(p<0.01) in slow oscillations (e.g., 1 and 3 Hz) and in parieto-centralregions (e.g., C1, C3, CZ, CP3, P1, PZ, P2, P4, POZ) in high frequencies(e.g., >40 Hz). B. Topographies depict area under the curve (AUC) of thereceiver operating characteristic (ROC) curve of change in relativepower of cortical oscillations in predicting cognitive change inresponse to seizure therapy at every electrode and frequency. The hotcolors illustrate higher AUC and better prediction. Change in power oflow frequency oscillations (e.g., 1-3 Hz) a provided good predictionvalue (0.8<AUC <0.9) such as in parieto-central retions (e.g., PZ, P2).Power of high frequency oscillations in the left motor cortex (i.e., C3electrode, 47 Hz) provided the best prediction value (AUC=0.9).

FIG. 10 (also referred to as Figure S4) shows the Effect of SeizureTherapy on Cortical Oscillations in Source Space. In all images, x-axisrepresents the frequency (1 to 50) in Hertz and y-axis representsRegions of Interest (ROTs) of the Destrieux Atlas (1 to 148). The ROIsare grouped into brain regions in the left (L: the upper half [of] theimages) and right (R: the lower half of the images) hemisphere separatedby the horizontal black line. Images show the post-hoc independentsample t-test statistics following cluster-based permutation testcorrection for multiple comparison, depicting only the significantclusters p<0.05, labeling only the significant corresponding ROIs, andsetting to 0 non-significant pixels. A. Image shows the t-teststatistics comparing the change in power between participants whoreceived ECT and MST interventions (red: more increase in ECT; blue:more reductions in ECT). This image depicts a significantly greaterincrease in slow oscillations (<10 Hz) and greater decrease in power offrequencies 20-50 Hz in the ECT group. This effect is spatially global.B. Image shows the independent sample t-test statistics comparing thechange in power between participants who were considered responders toseizure therapy (>=50% reduction in HAMD from baseline) andnon-responders. This image depicts a greater reduction in power offrequencies 20-50 Hz in responders. This finding is spatially global at−22 Hz, but more local in higher frequencies (30-50 Hz). Specifically in30-50 Hz, the reduction in power is observed in regions such as theinferior frontal sulcus, left orbital part of the frontal inferiorgyrus, bilateral preocciptial [preoccipital] notch, orbital gyri,lateral orbital sulcus, lateral occi-temporal sulcus, medial orbitalsulcus, bilateral paiieto-occiptial[paiieto-occipital] sulcus, orbilateral superior parietal lobule.

FIG. 11 (also referred to as Figure S5). The Association betweenCortical Oscillations and Mood and Cognition in Source Space. A, Imageillustrate the significant (p <0.05) spearman correlation coefficients(rho) between percent change in HAND and power in 34 patients receivingseizure therapy. All sources and frequencies that did not survive thecorrection for multiple comparisons were set to 0 (green colors). Onlysources that are significant have been listed. There were significantnegative clusters in tempro-parieto-occipital regions (e.g., orbitalsulci and gyri, bilateral dPCC, vPCC, precuneus, parieto-occipitalsulcus, occipital pole, inferior temporal gyms, lateral occi-temporalsulcus, etc.) in frequencies higher than 30 Hz. B. Image illustratesspearman correlation coefficients (rho) between percent change in MoCAand power across time-scales in 19 patients receiving seizure therapy.There was a global negative cluster in slow oscillations and a globalpositive association at 10 Hz frequency.

FIG. 12 (also referred to as Figure S6) shows The Association betweenChange in Complexity and Autobiographical Memory. A. Topographiesillustrate all the significant (original p<0.05) spearman correlationcoefficients (rho) between percent change in autobiographical memoryinterview (AMI) and multi scale entropy (MSE) across all time-scales foreach electrode. Cluster-based permutation test correction for multiplecomparison revealed a significant negative cluster in time-scales higherthan 40 across brain regions including the fronto-parietal regions. B.Topographies depict area under the curve (AUC) of the receiver operatingcharacteristic (ROC) curve of change in MSE in predicting change in AMIin response to seizure therapy at every electrode and time-scale. Thehot colors illustrate higher AUC and better prediction. Change incomplexity of coarse time-scales (e.g., >47) in fronto-parietal regionshad excellent (AUC range: 0.9 to 1.00) prediction performance.

FIG. 13 (also referred to as Figure S7) shows The Association betweenChange in Complexity and. Autobiographical Memory in Source Space. A.Image illustrates the significant (p<0.05) spearman correlationcoefficients (rho) between percent change in autobiographical memoryinterview (AMI) and multiscale entropy (MSE) at every Region of Interest(ROI) of the Destrieux Atlas (1 to 148) and each time-scale (1 to 70).All sources and scales that did not survive the correction for multiplecomparisons were set to 0 (green colors). Only sources that aresignificant have been listed. B. Image depicts the area under the curve(AUC) of the receiver operating characteristic (ROC) curve of change MSEin predicting change in AMI in response to seizure therapy at every ROIand each time-scale (1 to 70). Hot colors illustrate higher AUC andbetter prediction. Change in complexity of higher time-scales in severalbilateral frontal and parietal regions provided excellent (AUC range:0.9 to 1.00) prediction performance for change in AMI.

FIG. 14 shows the Effect of Escitalopram on Complexity of TemporalDynamics. Top: Images show the original post-hoc test statisticscomparing MSE pre (baseline) to post treatment (week 8) across allelectrodes (1 to 58) and all time-scales (1 to 70) (blue: increases;red: decreases following treatment) for responders (left) andnon-responders to escitalopram (right). Bottom. Each topography reflectsthe significant t-maps following correction for multiple comparison,using cluster-based non-parametric permutation test for p=0.05,depicting only the clusters p<0.09 and setting to 0 other pixels.Topographies highlight the spatial characteristics of the reduction ofMSE in fine time-scales in responders to escitalopram in fronto-parietalbrain regions (cluster p=0.09). No significant changes were observed innon-responders.

FIG. 15 shows the Association between Modulation of Temporal Complexityand Mood. Top. Image illustrate all the significant (original p<0.05)spearman correlation coefficients (rho) between percent change in MADRSand change in MSE (pre-post) in 95 patients receiving escitalopramtherapy. Cluster-based correction for multiple comparison resulted insignificant clusters (p<0.05) in parieto-occipital and fronto-centralregions in time-scale less than 10 factors. Bottom. Topographiesillustrate spatial distribution of this association

FIG. 16 shows the Escitalopram Induced Modulation of Complexity and. ItsAssociation with Mood in Source Space. In the left image, the x-axisrepresents the time scales (1 to 70) and y-axis represents Regions ofInterest (ROIs) of the Destrieux Atlas (1 to 148). Images show thepost-hoc test statistics following cluster-based permutation testcorrection for multiple comparison, depicting only the significantclusters p<0.05, labeling only the significant corresponding ROIs andsetting to 0 non-significant pixels. Scatter plots show that onlyregion-specific reduction in MSE in fine time-scales (less than 20 timescales) was significantly associated with enhancement of MADRS.

FIG. 17 show Differential Early Changes in Complexity of TemporalDynamics during Escitalopram Treatment in Responders and Non-Responders.Top. Images show the original post-hoc test statistics comparing MSE atweek 2 relative to pre treatment (baseline) across all electrodes (1 to58) and all time-scales (1 to 70) (blue: decreases; red: increasesfollowing treatment) for responders (Left column) and non-responders(Right column) from baseline to week 2. Bottom. Each topography reflectsthe significant t-maps following correction for multiple comparison,using cluster-based non-parametric permutation test, depicting only thesignificant clusters p<0.05 and setting to 0 non-significant pixels.Topographies highlight the spatial characteristics of the reduction ofMSE in coarse time-scales in non-responders. The reduction of MSE incoarse time-scales (e.g., scale factor >18) was localized to lateralfrontal, fronto-temporal, and parieto-occipital brain regions. Nosignificant changes were observed in non-responders.

FIG. 18 shows Source Localization of Early Changes in TemporalComplexity in Non-Responders to Escitalopram. X-axis represents the timescales (1 to 70) and y-axis represents Regions of Interest (ROIs) of theDestrieux Atlas (1 to 148). Images show the post-hoc test statisticsfollowing cluster-based permutation test correction for multiplecomparison, depicting only the significant clusters p<0.05, labelingonly the significant corresponding ROIs and setting to 0 non-significantpixels.

FIG. 19 show the Link Between Baseline Complexity and Change in Mood byEscitalopram. Top. Image illustrate all the significant (p<0.05)spearman correlation coefficients (rho) between percent change in MADRS(week 8 to baseline) and baseline MSE (pre-post) in 95 patientsreceiving escitalopram therapy. Cluster-based correction for multiplecomparison resulted in significant clusters (p<0.05) across multiplebrain regions (globe) such as in parieto-occipital and fronto-centralregions in time-scale higher than 37, Bottom. Topographies illustratespatial distribution of this association.

FIG. 20 shows the Link between Baseline Complexity and Change in Mood byEscitalopram in Source Space. Source analysis of data from prior figurereflects all brain region whose baseline complexity is associated withchange in depressive symptoms following Escitalopram therapy.

FIG. 21 shows the Link between Week 2 Complexity and Change in Mood byEscitalopram. Image illustrate all the significant (p<0.05) spearmancorrelation coefficients (rho) between percent change in MADRS (week 8to baseline) and week 2 MSE in 95 patients receiving escitalopramtherapy. Cluster-based correction for multiple comparison resulted insignificant clusters (p<0.05) across multiple brain regions. Bottom.Topographies illustrate spatial distribution of this association.

FIG. 22 shows the Link between Week 2 Complexity and Change in Mood byEscitalopram in Source Space. Source analysis of data from prior figurereflects all brain region whose complexity at week 2 of treatment isassociated with change in depressive symptoms following Escitalopram 8weeks of therapy.

What is claimed is:
 1. A method for treating depression in a subject inneed thereof, said method comprising: treating the subject by seizuretherapy administered through electroconvulsive therapy (ECT) or magneticseizure therapy (MST); and evaluating change in complexity of temporaldynamics in a brain of the subject following treatment to identifywhether complexity of fine time scale temporal dynamics in afronto-central and/or in a parieto-occipital region is reduced followingtreatment; wherein reduced complexity of fine time scale temporaldynamics in the fronto-central and/or parieto-occipital region followingtreatment identifies the subject as a responder to the seizure therapyfor treating depression; wherein the step of evaluating furthercomprises identifying change in complexity of coarse scale temporaldynamics in a parieto-central region following treatment, whereinreduced or maintained complexity of coarse scale temporal dynamics inthe parieto-central region following treatment identifies thatdeleterious cognitive side-effects of the seizure therapy in the subjectare limited.
 2. The method of claim 1, wherein complexity of temporaldynamics in the brain of the subject is evaluated usingelectroencephalography (EEG).
 3. The method of claim 1, wherein finetime scale temporal dynamics comprises less than 30 factors in theoccipital region of the brain.
 4. The method of claim 1, wherein theparieto-occipital region comprises at least a right occipital pole. 5.The method of claim 1, wherein coarse scale temporal dynamics comprisegreater than 50 factors, and less than 70 factors, in theparieto-central region of the brain.
 6. A method of monitoring theefficacy of an anti-depression treatment comprising seizure therapy in asubject having depression, said method comprising: obtaining a baselinemeasurement of the subject to determine baseline complexity of temporaldynamics in a brain of the subject, treating the subject with theanti-depression treatment comprising seizure therapy; and evaluatingchange in complexity of temporal dynamics in the brain of the subjectfollowing treatment to identify whether complexity of fine time scaletemporal dynamics in the fronto-central and/or parieto-occipital regionof the brain of the subject is reduced following the treatment; whereinreduced complexity of fine time scale temporal dynamics in thefronto-central and/or parieto-occipital region following treatmentidentifies the subject as a responder to the treatment, for which theanti-depression treatment is efficacious; wherein the step of evaluatingfurther comprises identifying change in complexity of coarse scaletemporal dynamics in the parieto-central region following the treatment,wherein reduced or maintained complexity of coarse scale temporaldynamics in the parieto-central region following treatment identifiesthat deleterious cognitive side-effects of the anti-depression treatmentin the subject are limited.
 7. The method of claim 6, wherein complexityof temporal dynamics in the brain of the subject is evaluated usingelectroencephalography (EEG).
 8. The method of claim 6, wherein finetime scale temporal dynamics comprises less than 30 factors in theoccipital region of the brain.
 9. The method of claim 6, wherein theparieto-occipital region comprises at least the right occipital pole.10. The method of claim 6, wherein coarse scale temporal dynamicscomprise greater than 50 factors, and less than 70 factors, in theparieto-central region of the brain.